An in Silico External Clinical Validation of AI Solutions for Cancer Management in the CHAIMELEON Project. Applied to 4 Target Types of Cancer (Lung, Breast, Prostate and Colorectal), Collected Through the Routine Delivery of Health Care With no Enrolment Conditinos (Real World Data).
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 300
- 试验地点
- 1
- 主要终点
- Usability of experimental viewer with AI tools
研究概览
简要总结
The goal of this observational study is to see how useful an experimental viewer and AI solutions are for clinicians in their daily work. The investigators want to find out if the AI helps clinicians interpret medical images for different types of cancer.
The AI solutions aim to:
- Classify whether prostate cancer is low or high risk
- Classify the histological subtype in breast cancer
- Estimate the life expectancy of patients with lung cancer
- Determine the size of colon cancer, lymph node involvement and the possibility of metastasis..
- Assess the invasion of sorrounding tissues in the case of rectum cancer. The study will involve clinicians from various centres who will review a set of cases not previously analysed by the AI. Clinicians will do this in two phases: first using only their own expertise and then with the help of the AI solutions.
The technical team want to see if the AI solutions assist clinicians and could become useful in the everyday clinical practice. Clinicians will complete a survey to share their feedback on the usability of the platform and how helpful the AI solutions are.
详细描述
In order to conduct a robust clinical validation, the investigators have designed a study on the required sample size. The study is design to evaluate the role of an AI-assisted tool as a support for improving the daily clinical work. The investigators used an online website (https://statulator.com/SampleSize/ss2PP.html) for the calculation and use the "paired binary proportions" option. Using the case of prostate cancer, the investigators want to compare the probability of correct risk classification in prostate cancer by clinicians alone and/or guided by AI. The study will have a significance (α) = 0.05; power (β) = 80%; the analysis will be "two sided" and with equal group sizes.
An 10% improvement in cancer risk classification was observed when clinicians had access to an AI tool solution (Yilmaz et al.,). In addition, the authors reported that expert readers had an accuracy rate of 81% compared to 69% for novice readers when determining the Gleason score of lesions (a medical term used in pathology to classify the aggressiveness of cells in a tumour). The authors also assumed an 80% correlation between paired observations.
As a result, at least 60 new cases would be needed to evaluate the performance of the AI tool.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Retrospective
入排标准
- 年龄范围
- 18 Years 至 85 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •patients with an histological confirmation of cancer diagnosis (prostate, lung, breast, colon or rectum)
- •availability of radiological images (MR for prostate and rectum, CT for lung and colon or mammographys for breast).
- •enough follow up (12 months for prostate, breast and rectum), 18 months for lung, and 24 months for colon.
排除标准
- •patients with incomplete or low quality data (radiological, pathological or uncomplete clinical data necessary for the ground truth)
结局指标
主要结局
Usability of experimental viewer with AI tools
时间窗: 5 months
Usability of the platform was assessed at the end of each of the two study phases: a standard clinical phase (without artificial intelligence assistance) and a second phase assisted by AI models. Participants evaluated their experience using a 5-point Likert scale, where 1 indicated "strongly disagree" and 5 indicated "strongly agree," in response to statements regarding ease of use, interface clarity, system efficiency, overall satisfaction, and other aspects related to user interaction with the platform. This assessment enabled a comparison of user perceptions of the viewer's usability under both conventional clinical conditions and AI-assisted conditions. Higher scores reflect a better user experience.
Utility of experimental medical images viewer
时间窗: 5 months
The utility of the experimental viewer was assessed by comparing clinicians' diagnostic accuracy and time spent when using the system alone versus with AI assistance. Higher accuracy and reduced interpretation time were considered indicators of greater utility. The goal was to determine whether the viewer enhances clinical decision-making, streamlines workflows, and supports better patient care. Additional data such as clinician gender, specialty, and experience were collected to enable subgroup analyses. Statistical evaluations included confusion matrices to assess diagnostic performance, and Sankey flow diagrams to visualize changes in decision-making between unaided and AI-assisted phases. These tools provided a comprehensive understanding of the viewer's practical benefit in real clinical scenarios.
次要结局
未报告次要终点